Machine learning based on Euclidean data, such as images, videos or audio signals, has enabled a revolution throughout science and engineering [1]}. The backbone of many success stories are deep neural networks and in particular convolutional neural networks. The extension of these techniques to non-Euclidean domains such as social networks, recommender systems, chemistry, computer graphics, NLP, and etc., has received a tremendous amount of attention [2]}. In many cases the data are represented as graphs, which are then embedded into graph neural networks (GNNs)[3]}, [4]}.
